{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:FCDTKKCH7SLKTHPFAK5PQRBKHT","short_pith_number":"pith:FCDTKKCH","schema_version":"1.0","canonical_sha256":"2887352847fc96a99de502baf8442a3cdf722070fbb18002b5d4ab75a6b7fa93","source":{"kind":"arxiv","id":"2506.17055","version":1},"attestation_state":"computed","paper":{"title":"Universal Music Representations? Evaluating Foundation Models on World Music Corpora","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR","cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Alexandros Potamianos, Charilaos Papaioannou, Emmanouil Benetos","submitted_at":"2025-06-20T15:06:44Z","abstract_excerpt":"Foundation models have revolutionized music information retrieval, but questions remain about their ability to generalize across diverse musical traditions. This paper presents a comprehensive evaluation of five state-of-the-art audio foundation models across six musical corpora spanning Western popular, Greek, Turkish, and Indian classical traditions. We employ three complementary methodologies to investigate these models' cross-cultural capabilities: probing to assess inherent representations, targeted supervised fine-tuning of 1-2 layers, and multi-label few-shot learning for low-resource s"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2506.17055","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2025-06-20T15:06:44Z","cross_cats_sorted":["cs.IR","cs.LG","eess.AS"],"title_canon_sha256":"9cce6dd631b8dde6da392f36a3dd22ff03a28c04de94df5ee276739659d61f0f","abstract_canon_sha256":"16a5b5a22f4cc0fb0ed46b58807703af603aeceda19e31162e04c39f68465c76"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:24:55.624439Z","signature_b64":"v+a2F3GYuwWjPxNcbUsVcdgB6f2EYtBfpmSZ9JWIFoRaR1pwiEFqTQ//6tsQM2rb4mPRapu/JvDDio2pKoUiDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2887352847fc96a99de502baf8442a3cdf722070fbb18002b5d4ab75a6b7fa93","last_reissued_at":"2026-07-05T11:24:55.623987Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:24:55.623987Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Universal Music Representations? Evaluating Foundation Models on World Music Corpora","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR","cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Alexandros Potamianos, Charilaos Papaioannou, Emmanouil Benetos","submitted_at":"2025-06-20T15:06:44Z","abstract_excerpt":"Foundation models have revolutionized music information retrieval, but questions remain about their ability to generalize across diverse musical traditions. This paper presents a comprehensive evaluation of five state-of-the-art audio foundation models across six musical corpora spanning Western popular, Greek, Turkish, and Indian classical traditions. We employ three complementary methodologies to investigate these models' cross-cultural capabilities: probing to assess inherent representations, targeted supervised fine-tuning of 1-2 layers, and multi-label few-shot learning for low-resource s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.17055","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2506.17055/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2506.17055","created_at":"2026-07-05T11:24:55.624034+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.17055v1","created_at":"2026-07-05T11:24:55.624034+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.17055","created_at":"2026-07-05T11:24:55.624034+00:00"},{"alias_kind":"pith_short_12","alias_value":"FCDTKKCH7SLK","created_at":"2026-07-05T11:24:55.624034+00:00"},{"alias_kind":"pith_short_16","alias_value":"FCDTKKCH7SLKTHPF","created_at":"2026-07-05T11:24:55.624034+00:00"},{"alias_kind":"pith_short_8","alias_value":"FCDTKKCH","created_at":"2026-07-05T11:24:55.624034+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.00777","citing_title":"Evaluating Pretrained Music Embeddings for Cross-Performance Jazz Standard Recognition","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2605.03395","citing_title":"APEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Music","ref_index":45,"is_internal_anchor":false},{"citing_arxiv_id":"2605.03395","citing_title":"APEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Music","ref_index":45,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FCDTKKCH7SLKTHPFAK5PQRBKHT","json":"https://pith.science/pith/FCDTKKCH7SLKTHPFAK5PQRBKHT.json","graph_json":"https://pith.science/api/pith-number/FCDTKKCH7SLKTHPFAK5PQRBKHT/graph.json","events_json":"https://pith.science/api/pith-number/FCDTKKCH7SLKTHPFAK5PQRBKHT/events.json","paper":"https://pith.science/paper/FCDTKKCH"},"agent_actions":{"view_html":"https://pith.science/pith/FCDTKKCH7SLKTHPFAK5PQRBKHT","download_json":"https://pith.science/pith/FCDTKKCH7SLKTHPFAK5PQRBKHT.json","view_paper":"https://pith.science/paper/FCDTKKCH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.17055&json=true","fetch_graph":"https://pith.science/api/pith-number/FCDTKKCH7SLKTHPFAK5PQRBKHT/graph.json","fetch_events":"https://pith.science/api/pith-number/FCDTKKCH7SLKTHPFAK5PQRBKHT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FCDTKKCH7SLKTHPFAK5PQRBKHT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FCDTKKCH7SLKTHPFAK5PQRBKHT/action/storage_attestation","attest_author":"https://pith.science/pith/FCDTKKCH7SLKTHPFAK5PQRBKHT/action/author_attestation","sign_citation":"https://pith.science/pith/FCDTKKCH7SLKTHPFAK5PQRBKHT/action/citation_signature","submit_replication":"https://pith.science/pith/FCDTKKCH7SLKTHPFAK5PQRBKHT/action/replication_record"}},"created_at":"2026-07-05T11:24:55.624034+00:00","updated_at":"2026-07-05T11:24:55.624034+00:00"}